When people read, hear, or prepare research summaries,
they sometimes have misconceptions about what is or isn't "sound
practice" regarding the collection, analysis, and interpretation
of data. Here are some of these common (and dangerous) misconceptions
associated with the content of Chapter 16.

In regression analyses, the independent variable
is the variable being predicted.

In a bivariate regression, it's important to statistically
test both r and b.

If a bivariate regression is statistically significant,
its equation can be used to make predictions for any
X scores.

In a discussion of bivariate regression, the notation
"SEM" stands for "standard error of the mean."

In a multiple regression, the more predictor variables
the better.

Multiple regression works best when the independent
variables are highly intercorrelated.

In a hierarchical multiple regression, the coefficients
determined in the 1st stage of the analysis remain constant throughout
the analysis.

In a multiple regression study, the researcher should
always test DR2.

An odds ratio will be equal to zero if, in the sample
data, the independent variable being focused upon is worthless.

The Wald test in logistic regression is like a test
of R2 in multiple regression.